Understanding the Roots of Radicalisation on Twitter

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation

An Examination of Dynamic Risk, Protective Factors, and Treatment-Related Change in Violent Offenders

The present study was archival in nature and examined risk for recidivism, treatment-related changes in risk, protection against recidivism, treatment-related changes in protection, the relationship between risk and protective factors, and the prediction of positive community outcomes. A select set of risk- and protective-factor measures were used, including the Violence Risk Scale (VRS), the Historical Clinical Risk Management scheme-20 (HCR-20 version 2), the Structured Assessment of Protective Factors (SAPROF), and the PF List (an operationalized list of protective factors developed by the investigators). Participants included 178 federally incarcerated adult male violent offenders who participated in the Aggressive Behaviour Control treatment program at the Regional Psychiatric Centre (Saskatoon, SK) between 1998 and 2003. Participants were followed for an average of 9.7 years (SD 2.6) to assess community recidivism. Approximately 60% had at least one new violent conviction, 60% had at least one new nonsexual violent conviction, and 79% had at least one new conviction (i.e., any reconviction). Additionally, participants were followed for an average of 30.7 months (SD = 40.3) to assess institutional recidivism. Approximately 31% had at least one post-treatment major misconduct, 51% had at least one post-treatment minor misconduct, 12% had at least one post-treatment violent misconduct, and 56% had at least one post-treatment misconduct (i.e., any misconduct). Correlations between the risk measures scores support their convergent validity. Both the VRS and HCR-20 predicted all violent, nonsexual violent, and any recidivism. Dynamic variables on these tools generally added uniquely to the prediction of community recidivism over static variables. A similar but weaker pattern of results was observed for institutional recidivism. Additionally, treatment-related change scores on the risk measures added uniquely to the prediction of most recidivism outcomes, supporting the dynamism of these tools and the hypothesis that treatment-related changes translate to actual reductions in recidivism rates. Correlations between the protection measures’ scores support their convergent validity. The protective factor tools, the SAPROF and PF List, similarly predicted community recidivism and, to a lesser degree, institutional recidivism. Dynamism of the protective factor tools was supported and change scores on these tools added incrementally to the prediction of recidivism outcomes. Large correlations were observed between the risk and protection scores, suggesting that part of the predictive accuracy of the protection measures may relate to measuring the absence of risk rather than the presence of protection. Alternative hypotheses are discussed. Protection scores did not add incrementally to the prediction of recidivism over their respective risk scores. Risk, protection, and change scores were significant predictors of most positive community outcomes. Protection scores and risk change scores added incrementally to the prediction of positive community outcomes over their respective risk scores. As such, it appears that treatment-related changes may also represent increases in other positive community outcomes (beyond reduced reoffending) and that protection factors may have important benefits in risk assessment and treatment planning when other positive community outcomes are considered. Strengths, limitations, and implications are discussed

The Cross Race Effect: The Influence of Stereotypicality on Memory Errors

In eyewitness identification cases, suspect misidentification is the leading factor attributed to wrongful convictions (Scheck, Neufeld, & Dwyer, 2000), thus, it is of applied importance to identify factors that contribute to the false recollection of faces. One potential factor addressed in the current study was whether face memory and subsequent identification for other-race-faces is biased by the degree to which a target face posses facial features associated with ethnic identity. Individual differences in level of processing (global, local) and prejudice were tested as potential mechanisms contributing to biased judgments. In Experiment 1 a standard face recognition task revealed that prejudice, level of processing, and face-type interacted to predict recognition bias. In Experiment 2 results showed that positive misidentifications (i.e., choosing an incorrect foil) were more likely when a stereotypical versus non-stereotypical Black actor was witnessed committing the crime. Results are discussed in terms of theoretical and practical implications

Humane Metrics/Metrics Noir

That Elsevier/RELX group has now rebranded itself as a “global provider of information and analytics,” seems indicative of the way academic publishing is increasingly moving into the highly pro table data analytics market. Here the linking of journals and scholarly social networks to the data underlying them through article level metrics, citation and download gures, usage statistics, ratings and altmetrics, serves as an opportunity to further extract value from the relationalities of scholarly publishing. Connect this to the demand of neoliberal governments for bibliometrics to index and rank scholars and their universities in order to measure impact and excellence, and enable accountability and transparency as part of national research assessment exercises, and it is clear that the logic of calculation and its accompanying mechanisms of surveillance and control is now omnipresent in scholarly publishing—and this includes requirements towards researchers to measure and monitor themselves as “brands.” The texts in this pamphlet will ask, what are the implications of this state of a airs for scholarship and for the value of expertise and democratic judgement? Is it indeed the case that, as Chris Newfield argues “with indicators ascendant over judgment itself, and tied to complicated, obscure, or proprietary procedures, metrics can pacify the interpretive powers of the public and professionals alike”? Yet the authors of this pamphlet will also explore strategies for pushing back against the metrification of scholarship and publishing